Computing / IT
Machine Learning Engineer interview questions:
The role of machine learning engineer is highly technical which makes it appealing to companies that have data-driven components in their product line. Additionally, machine learning engineers’ practical skills resemble those needed for a data scientist role, however, are more particularly interested in tackling real-world challenges through the creation and deployment of machine learning models. Therefore, this requires the machine learning engineer to have both the academic background and practical hands-on experience of machine learning and be specifically well-versed in fields such as; statistics, optimization, data mining and algorithmic design.
They are aware of the steps they need to take to choose the right type of model to solve the problem at hand amongst a wide range of multiple models at their disposal. Their observation and analysis of each model, allows them to understand the limitations and assumptions of each model and to be able to develop specific aspects to improve model performance by utilizing the right metrics to measure and advance model accuracy. One of the core important skills for this role is having a strong research background which comes with having a PhD in the field and this why a PhD degree is an asset. On the other hand, the practical aspect of the role requires candidates to be well versed in using specialized tools and packages for machine learning. For instance, scikit-learn (Python), Spark ML, R, Mahout and so on. An important note is that candidates will most likely utilize a lot of their computer science or statistics background.
You could start and develop the technical conversation by asking your candidates to describe how a specific model that used previously works. Furthermore, technical interviews can be quite stressful and overwhelming for prospective candidates and this is why it is important to create a comfortable environment for them by giving them the opportunity to speak about something they are well versed in and have experience working with. Even if the model chosen by them is very simple, it is fine because the goal of the technical conversation is to check if the candidate is fully aware of the model they are talking about and not only the basics. Therefore, diving deep into describing a model as simple as k-nearest neighbors or linear regression can disclose a lot of information about a candidate.
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